Adding some quality of life for pipeline function. (#14322)

* Adding some quality of life for `pipeline` function.

* Update docs/source/main_classes/pipelines.rst

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/pipelines/__init__.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Improve the tests.

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

docs/source/main_classes/pipelines.rst

@@ -45,7 +45,7 @@ The pipeline abstraction
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The `pipeline` abstraction is a wrapper around all the other available pipelines. It is instantiated as any other
-pipeline but requires an additional argument which is the `task`.
+pipeline but can provide additional quality of life.
 
 Simple call on one item:
 
@@ -55,6 +55,15 @@ Simple call on one item:
     >>> pipe("This restaurant is awesome")
     [{'label': 'POSITIVE', 'score': 0.9998743534088135}]
 
+If you want to use a specific model from the `hub <https://huggingface.co>`__ you can ignore the task if the model on
+the hub already defines it:
+
+.. code-block::
+
+    >>> pipe = pipeline(model="roberta-large-mnli")
+    >>> pipe("This restaurant is awesome")
+    [{'label': 'POSITIVE', 'score': 0.9998743534088135}]
+
 To call a pipeline on many items, you can either call with a `list`.
 
 .. code-block::
@@ -226,6 +235,32 @@ For users, a rule of thumb is:
       - The larger the GPU the more likely batching is going to be more interesting
 - As soon as you enable batching, make sure you can handle OOMs nicely.
 
+Pipeline custom code
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+If you want to override a specific pipeline.
+
+Don't hesitate to create an issue for your task at hand, the goal of the pipeline is to be easy to use and support most
+cases, so :obj:`transformers` could maybe support your use case.
+
+
+If you want to try simply you can:
+
+- Subclass your pipeline of choice
+
+.. code-block::
+
+    class MyPipeline(TextClassificationPipeline):
+        def postprocess(...):
+            ...
+            scores = scores * 100
+            ...
+
+    my_pipeline = MyPipeline(model=model, tokenizer=tokenizer, ...)
+    # or if you use `pipeline` function, then:
+    my_pipeline = pipeline(model="xxxx", pipeline_class=MyPipeline)
+
+That should enable you to do all the custom code you want.
 
 
 Implementing a pipeline

src/transformers/pipelines/__init__.py

@@ -2,6 +2,9 @@
 # There's no way to ignore "F401 '...' imported but unused" warnings in this
 # module, but to preserve other warnings. So, don't check this module at all.
 
+import io
+import json
+
 # coding=utf-8
 # Copyright 2018 The HuggingFace Inc. team.
 #
@@ -21,7 +24,7 @@ from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union
 
 from ..configuration_utils import PretrainedConfig
 from ..feature_extraction_utils import PreTrainedFeatureExtractor
-from ..file_utils import is_tf_available, is_torch_available
+from ..file_utils import http_get, is_tf_available, is_torch_available
 from ..models.auto.configuration_auto import AutoConfig
 from ..models.auto.feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
 from ..models.auto.tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer
@@ -248,6 +251,29 @@ SUPPORTED_TASKS = {
 }
 
 
+def get_task(model: str, use_auth_token: Optional[str] = None) -> str:
+    tmp = io.BytesIO()
+    headers = {}
+    if use_auth_token:
+        headers["Authorization"] = f"Bearer {use_auth_token}"
+
+    try:
+        http_get(f"https://huggingface.co/api/models/{model}", tmp, headers=headers)
+        tmp.seek(0)
+        body = tmp.read()
+        data = json.loads(body)
+    except Exception as e:
+        raise RuntimeError(f"Instantiating a pipeline without a task set raised an error: {e}")
+    if "pipeline_tag" not in data:
+        raise RuntimeError(
+            f"The model {model} does not seem to have a correct `pipeline_tag` set to infer the task automatically"
+        )
+    if data.get("library_name", "transformers") != "transformers":
+        raise RuntimeError(f"This model is meant to be used with {data['library_name']} not with transformers")
+    task = data["pipeline_tag"]
+    return task
+
+
 def check_task(task: str) -> Tuple[Dict, Any]:
     """
     Checks an incoming task string, to validate it's correct and return the default Pipeline and Model classes, and
@@ -299,7 +325,7 @@ def check_task(task: str) -> Tuple[Dict, Any]:
 
 
 def pipeline(
-    task: str,
+    task: str = None,
     model: Optional = None,
     config: Optional[Union[str, PretrainedConfig]] = None,
     tokenizer: Optional[Union[str, PreTrainedTokenizer]] = None,
@@ -309,6 +335,7 @@ def pipeline(
     use_fast: bool = True,
     use_auth_token: Optional[Union[str, bool]] = None,
     model_kwargs: Dict[str, Any] = None,
+    pipeline_class: Optional[Any] = None,
     **kwargs
 ) -> Pipeline:
     """
@@ -422,6 +449,14 @@ def pipeline(
     """
     if model_kwargs is None:
         model_kwargs = {}
+
+    if task is None and model is None:
+        raise RuntimeError(
+            "Impossible to instantiate a pipeline without either a task or a model"
+            "being specified."
+            "Please provide a task class or a model"
+        )
+
     if model is None and tokenizer is not None:
         raise RuntimeError(
             "Impossible to instantiate a pipeline with tokenizer specified but not the model "
@@ -435,9 +470,18 @@ def pipeline(
             "Please provide a PreTrainedModel class or a path/identifier to a pretrained model when providing feature_extractor."
         )
 
+    if task is None and model is not None:
+        if not isinstance(model, str):
+            raise RuntimeError(
+                "Inferring the task automatically requires to check the hub with a model_id defined as a `str`."
+                f"{model} is not a valid model_id."
+            )
+        task = get_task(model, use_auth_token)
+
     # Retrieve the task
     targeted_task, task_options = check_task(task)
-    task_class = targeted_task["impl"]
+    if pipeline_class is None:
+        pipeline_class = targeted_task["impl"]
 
     # Use default model/config/tokenizer for the task if no model is provided
     if model is None:
@@ -549,4 +593,4 @@ def pipeline(
     if feature_extractor is not None:
         kwargs["feature_extractor"] = feature_extractor
 
-    return task_class(model=model, framework=framework, task=task, **kwargs)
+    return pipeline_class(model=model, framework=framework, task=task, **kwargs)

tests/test_pipelines_common.py

@@ -29,8 +29,10 @@ from transformers import (
     AutoTokenizer,
     IBertConfig,
     RobertaConfig,
+    TextClassificationPipeline,
     pipeline,
 )
+from transformers.pipelines import get_task
 from transformers.pipelines.base import _pad
 from transformers.testing_utils import is_pipeline_test, require_torch
 
@@ -261,6 +263,29 @@ class CommonPipelineTest(unittest.TestCase):
         for output in text_classifier(dataset):
             self.assertEqual(output, {"label": ANY(str), "score": ANY(float)})
 
+    @require_torch
+    def test_check_task_auto_inference(self):
+        pipe = pipeline(model="Narsil/tiny-distilbert-sequence-classification")
+
+        self.assertIsInstance(pipe, TextClassificationPipeline)
+
+    @require_torch
+    def test_pipeline_override(self):
+        class MyPipeline(TextClassificationPipeline):
+            pass
+
+        text_classifier = pipeline(model="Narsil/tiny-distilbert-sequence-classification", pipeline_class=MyPipeline)
+
+        self.assertIsInstance(text_classifier, MyPipeline)
+
+    def test_check_task(self):
+        task = get_task("gpt2")
+        self.assertEqual(task, "text-generation")
+
+        with self.assertRaises(RuntimeError):
+            # Wrong framework
+            get_task("espnet/siddhana_slurp_entity_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best")
+
 
 @is_pipeline_test
 class PipelinePadTest(unittest.TestCase):